Summary:[unreadable] Effect of Nonsynonymous Single Nucleotide Polymorphisms on DNA Repair Capacity: Nonsynonymous single nucleotide polymorphisms (nsSNPs) are a type of genetic variation that changes the amino acid sequence of a protein. If the resulting amino acid change affects protein function, the altered protein may play a role in the initiation or progression of complex multifactorial diseases such as cancer. We evaluated more than 1500 candidate nsSNPs within 152 DNA repair-associated genes with the goal of identifying nsSNPs that alter the response of lymphoblastoid cell lines (LCLs) to DNA damaging agents. If a functional nsSNP were identified in a DNA repair associated gene it may represent a genetic risk factor for developing cancer. We used, in silico prediction algorithms to suggest what effect the new amino acid might have on the function of the protein. These predictions are based on sequence conservation of that residue among species, proximity of the altered amino acid to known active sites, and possible perturbation of the microenvironment within the tertiary structure of the protein based on steric hindrance, charge reversals, or polarity changes caused by the new amino acid. [unreadable] Based on this analysis we chose two nsSNPs for study one in ATM, which encodes the damage sensor, cell cycle regulator, and PI3 kinase, and the other in POLQ, the gene encoding the translesion DNA polymerase theta involved in bypassing abasic DNA lesions. We used gamma irradiation and hydrogen peroxide exposure as damaging agents in the ATM and POLQ SNP studies, respectively. In each case the single cell gel electrophoresis assay, or comet assay, was used to assess changes in DNA repair capacity between LCLs harboring different alleles of the nsSNP being investigated. [unreadable] [unreadable] Integrating GWAS and candidate gene information with a functional SNP Selection Algorithm in the Genetic Determinants of Prostate Cancer Aggressiveness: Genome-wide association studies (GWAS) and candidate pathway studies provide complementary types of information and have different strengths and limitations. With technologies and statistical methods for both of these approaches rapidly advancing, investigators face challenges in mining enormous databases for relevant information. We designed and implemented a set of decision rules for SNP selection that allows an investigator to specify a list of genes or linkage regions and to select SNPs based both on GWAS results and on predicted functional characteristics for both coding and non-coding SNPs. It provides a means of eliminating genes and SNPs that have shown limited significance based on GWAS studies and, for genes that are selected, to choose additional predicted functional SNPs that (based on LD) have not been evaluated in existing GWAS panels. We implemented this method using prostate cancer where, starting with more than 300 genes from existing candidate gene and expression studies we eliminate genes that have been adequately evaluated in a prostate GWAS panel. We then use GWAS data to select SNPs in the remaining genes based on function (likely to cause damaging amino acid changes or modify regulatory elements) and add tag SNPs for LD bins in selected genes that have never been evaluated in GWAS. Using more stringent functional criteria we can select additional SNPs from the entire GWAS data. In this way, we construct a panel of 1,536 SNPs, more than 50% of which have never been evaluated in GWAS, which are enriched SNPs with likely functional effects and are testing these in a case-case comparison of prostate cancer aggressiveness. The method is scaleable to any size SNP panel and can choose SNPs across multiple ethnic groups.